Open Source in Environmental Sustainability#

fig = go.Figure(
    data=[
        go.Table(
            header=dict(values=["Dimension", "Value"],line_color='#000000',
                        fill_color='#ffffff', font_size=18 ,  ),
            cells=dict(
                        fill_color='#ffffff',
                        line_color='#ffffff',
                        font_size=16,
                        height=30,
                values=[
                    [
                        "Total number of projects",
                        "Github projects",
                        "Gitlab projects",
                        "Other platforms",
                        "Number of projects in personal namespace",
                        "Total stars of all projects",
                        "Total contributers of all projects",
                        "Active GitHub projects",
                        "Inactive GitHub projects",
                        "Projects with contribution guide in %",
                        "Projects with code of conduct in %",
                        "Projects accepting donations in %",
                        "Median number of commits",
                        "Median stargazers",
                        "Median stars last year",
                        "Median Development Distribution Score",
                        "Median number of contributors",
                        "Median closed issues last year",
                        "Median commits last year",
                        "Median age in years",
                    ],
                    [
                        df_raw["project_name"].count(),
                        df_raw["platform"].value_counts()["github"],
                        df_raw["platform"].value_counts()["gitlab"],
                        df_raw["platform"].value_counts()["custom"],
                        df_raw["project_name"].count() - df_raw["organization"].count(),
                        df_raw["stargazers_count"].sum(),
                        df_raw["contributors"].sum(),
                        df_raw["project_active"].value_counts()[True],
                        df_raw["project_active"].value_counts()[False],
                        round(df_raw["contribution_guide"].value_counts(normalize=True)[True]*100,2),
                        round(df_raw["code_of_conduct"].value_counts(normalize=True)[True]*100,2),
                        round(df_raw["accepts_donations"].value_counts(normalize=True)[True]*100,2),
                        df_raw["total_number_of_commits"].median(),
                        df_raw["stargazers_count"].median(),
                        df_raw["stars_last_year"].median(),
                        round(df_raw["development_distribution_score"].median(),4),
                        df_raw["contributors"].median(),
                        df_raw["issues_closed_last_year"].median(),
                        df_raw["total_commits_last_year"].median(),
                        round(df_raw["project_age_in_years"].median(),2),
                        
                    ],
                ]
            ),
        )
    ]
)



fig.update_layout(
height=700,
width=700
)
fig.show()

### KK: this is where a clear object naming convention + comments would really help: is syntax df[df_raw[..]] appropriate here? 
### KK: it might be helpful to plot boxplots for the below scores per category to better show their distribution, including median

df_personal_projects = df_active[df_active["organization"].isna()]
df_organization_projects = df_active[df_active["organization"].notna()]
df_inactive = df_raw[(df_raw["project_active"] == False)]
df_top_stargazers = df_active[(df_active["stargazers_count"] > 100)]

fig = go.Figure(
    data=[
        go.Table(
            header=dict(values=["Group", "Median DDS"],line_color='#000000',fill_color='#ffffff',font_size=18),
            cells=dict(
                        line_color='#ffffff',fill_color='#ffffff', font_size=16, height =30,
                values=[
                    [
                        "All projects",
                        "Active projects in personal namespace",
                        "Active organization projects",
                        "Active projects",
                        "Inactive projects",
                        "Active projects over than 50 Stars",
                        "Projects with most contributors"

                    ],
                    [
                        round(df_raw["development_distribution_score"].median(),3),
                        round(df_personal_projects["development_distribution_score"].median(),3),
                        round(df_organization_projects["development_distribution_score"].median(),3),
                        round(df_active["development_distribution_score"].median(),3),
                        round(df_inactive["development_distribution_score"].median(),3),
                        round(df_top_stargazers["development_distribution_score"].median(),3),
                        round(df_active.nlargest(50, "contributors")["development_distribution_score"].median(),3)
                    ],
                ]
            ),
        )
    ]
)

fig.update_layout(
width=700,
height=300

)

fig.show()

license_his = (
    df_active["license"]
    .value_counts()
    .to_frame()
    .rename_axis("license_names")
    .reset_index()
)
fig = px.pie(license_his, values="license", names="license_names", color_discrete_sequence=color_discrete_sequence, hole=0.2)

fig.update_layout(title="Distribution of Licenses", showlegend=False, font_size=16)
fig.update_traces(textposition='inside', textinfo='percent+label', marker=dict(line=dict(color='#000000', width=1)))
fig.show()

# alternative to the pie chart in cell 23
# main point: ~80% of all open source licences fall under 5 types
main_license_types = ['BSD-3-Clause', 'MIT', 'GPL-3.0', 'CUSTOM', 'Apache-2.0']
alt_df_active = df_active.copy()

alt_df_active['pooled_license'] = np.where(
     alt_df_active['license'].isin(main_license_types), alt_df_active['license'], 'Other')

alt_license_his = (
    alt_df_active["pooled_license"]
    .value_counts()
    .to_frame()
    .rename_axis("license_names")
    .reset_index()
)
alt_fig = px.pie(alt_license_his, values="pooled_license", names="license_names", color_discrete_sequence=color_discrete_sequence, hole=0.2)

alt_fig.update_layout(title="Distribution of Licenses", showlegend=False, font_size=16)
alt_fig.update_traces(textposition='inside', textinfo='percent+label', marker=dict(line=dict(color='#000000', width=1)))
alt_fig.show()

fig = px.histogram(
    df_active,
    x="project_age_in_years",
    nbins=50,
    title="Distribution of Project Age in Years",
)
fig.update_layout(
    yaxis_title= None,
    xaxis_title="Project Age",
)
fig.update_traces(marker_color=marker_color)
fig.show()

fig = px.histogram(
    df_active,
    x="total_number_of_commits",
    nbins=50,
    title="Distribution of Total Commits",
)
fig.update_layout(
    yaxis_title="Projects",
    xaxis_title="Project Total Commits",
)
fig.update_traces(marker_color=marker_color)
fig.show()

topic_his = (
    df_active["topic"]
    .value_counts()
    .to_frame()
    .rename_axis("topic_names")
    .reset_index()
)

fig = px.bar(
    df_active.groupby('topic')['contributors'].sum().reset_index().sort_values('contributors',ascending=[False]),
    x="contributors",
    y="topic",
    orientation="h",
)

fig.update_layout(
    height=1000,  # Added parameter
    yaxis_title= None,
    xaxis_title="Contributors",
    title="Contributors within Topics",
    coloraxis_colorbar=dict(
    title="DDS",
    ),
    hoverlabel=dict(
    bgcolor="white"
    )
)
fig.update_traces(marker_color=marker_color)
fig.update(layout_showlegend=False)

topic_his = (
    df_active["topic"]
    .value_counts()
    .to_frame()
    .rename_axis("topic_names")
    .reset_index()
)

fig = px.bar(
    topic_his,
    x="topic",
    y="topic_names",
    orientation="h",
)

fig.update_layout(
    height=1000,  # Added parameter
    yaxis_title= None,
    xaxis_title="Projects",
    title="Projects within Topics",
    coloraxis_colorbar=dict(
    title="DDS",
    ),
    hoverlabel=dict(
    bgcolor="white"
    )
)
fig.update_traces(marker_color=marker_color)
fig.update(layout_showlegend=False)

license_dominating_language = (
    df_active["dominating_language"]
    .value_counts()
    .to_frame()
    .rename_axis("dominating_language_names")
    .reset_index()
)
license_dominating_language
license_dominating_language = license_dominating_language[(license_dominating_language["dominating_language"] > 4)]
fig = px.pie(license_dominating_language, values="dominating_language", names="dominating_language_names", color_discrete_sequence=color_discrete_sequence, hole=0.2)

fig.update_layout(title="Distribution of Programming Languages", showlegend=False, font_size=16)
fig.update_traces(textposition='inside', textinfo='percent+label', marker=dict(line=dict(color='#000000', width=1)))
fig.show()

# KK I thing the question that we should be asking: are there similar patterns followed by most topics? If so, whatare they? If not, what are the fields that stand out and what is the difference?

# df_sorted = df.groupby(['topic'], as_index=False)['dominating_language'].agg('sum')
df_language_distribution = (
    df_active.value_counts(["topic", "dominating_language"]).to_frame().reset_index()
)

df_language_distribution.rename(columns={0: "counts"}, inplace=True)
fig = px.scatter(
    df_language_distribution, x="dominating_language", y="topic", size="counts", 
)


fig.update_layout(
    height=1000,  # Added parameter
    width=1200,
    xaxis_title= None,
    yaxis_title= None,
)
fig.update_traces(marker_color=marker_color)


fig.show()

# KK I thing the question that we should be asking: are there similar patterns followed by most topics? If so, whatare they? If not, what are the fields that stand out and what is the difference?

# df_sorted = df.groupby(['topic'], as_index=False)['dominating_language'].agg('sum')
df_license_distribution = (
    df_active.value_counts(["topic", "license"]).to_frame().reset_index()
)

df_license_distribution.rename(columns={0: "counts"}, inplace=True)
fig = px.scatter(df_license_distribution, x="license", y="topic", size="counts")


fig.update_layout(
    height=1000,  # Added parameter
    xaxis_title="License",
    yaxis_title=None,
    title= None,
    autosize=True,
)
fig.update_traces(marker_color=marker_color)


fig.show()

fig = px.histogram(
    df_active,
    x="contributors",
    nbins=100,
    title=" Contributors",
)
fig.update_layout(
    yaxis_title="Projects",
    xaxis_title="Contributors",
)
fig.update_traces(marker_color=marker_color)
fig.show()

most_listed_projects = df_active["git_namespace"].value_counts(ascending=False).to_frame().rename_axis("Namespace").reset_index().rename(columns={"git_namespace": "counts"})
fig = go.Figure(data=[go.Table(
    header=dict(values=list(most_listed_projects.columns), line_color='#000000', fill_color='#ffffff',font_size=18 ),
    cells=dict(line_color='#ffffff', fill_color='#ffffff', font_size=16, height=30, values=[most_listed_projects.Namespace, most_listed_projects.counts])
)])

fig.update_layout(
autosize=False,
)

fig.show()

oldest_projects = df_active.nlargest(40, "project_age_in_years")


fig = px.bar(
    oldest_projects,
    x=oldest_projects["project_age_in_years"],
    y=oldest_projects["project_name"],
    orientation="h",
    range_x=(9.6, 14),
    hover_name=oldest_projects["git_url"],
    hover_data=["oneliner","topic","git_namespace"],
    color=oldest_projects["development_distribution_score"],
    color_continuous_scale=color_continuous_scale
)

fig.update_layout(
    height=1000,  # Added parameter
    yaxis_title=None,
    xaxis_title="Project Age in Years",
    title="The oldest Projects still active",
    coloraxis_colorbar=dict(
    title="DDS",
    ),
    hoverlabel=dict(
    bgcolor="white"
    )
)



fig.update(layout_showlegend=False)

contributors = df_active.nlargest(40, "contributors")

fig = px.bar(
    contributors,
    x=contributors["contributors"],
    y=contributors["project_name"],
    orientation="h",
    title="Projects with most contributors",
    hover_name=contributors["git_url"],
    hover_data=["oneliner","topic","git_namespace"],
    color=contributors["development_distribution_score"],
    color_continuous_scale=color_continuous_scale
)

fig.update_layout(
    height=1200,  # Added parameter
    xaxis_title="Contributors",
    yaxis_title= None,
    title="Projects with the most contributors",
    coloraxis_colorbar=dict(
    title="DDS",
    ),
    hoverlabel=dict(
    bgcolor="white"
    )
)

fig.update(layout_showlegend=False)

top_stargazers = df_active.nlargest(40, "stargazers_count")

fig = px.bar(
    top_stargazers,
    x=top_stargazers["stargazers_count"],
    y=top_stargazers["project_name"],
    orientation="h",
    hover_name=top_stargazers["git_url"],
    hover_data=["oneliner","topic","git_namespace"],
    color=top_stargazers["development_distribution_score"],
    color_continuous_scale=color_continuous_scale

)

fig.update_layout(
    height=1000,  # Added parameter
    xaxis_title="Stars",
    yaxis_title=None,
    title="Projects with the most Stars",
    coloraxis_colorbar=dict(
    title="DDS",
    ),
    hoverlabel=dict(
    bgcolor="white"
    )
)

fig.update(layout_showlegend=False)

df_top_100_stargazers = df_active[(df_active["stargazers_count"]) > 100].copy()
df_top_100_stargazers["star_growth"] = (
    df_top_100_stargazers["stars_last_year"] / df_top_100_stargazers["stargazers_count"]
)

df_top_40_star_growth = df_top_100_stargazers.nlargest(40, "star_growth")
fig = px.bar(
    df_top_40_star_growth,
    x=df_top_40_star_growth["star_growth"] * 100,
    y=df_top_40_star_growth["project_name"],
    orientation="h",
    hover_name=df_top_40_star_growth["git_url"],
    hover_data=["oneliner","topic","git_namespace"],
    color=df_top_40_star_growth["development_distribution_score"],
    color_continuous_scale=color_continuous_scale
)

fig.update_layout(
    height=1000,  # Added parameter
    xaxis_title="Star Growth last Year [%]",
    yaxis_title= None,
    title="Projects with the highest Star Growth",
    hoverlabel=dict(
    bgcolor="white"),
    coloraxis_colorbar=dict(
    title="DDS",
    ),
)

df_top_40_growth = df_active.nlargest(40, "total_commits_last_year")
df_top_40_growth = df_top_40_growth[df_top_40_growth["project_name"] != "ElexonDataPortal"]
fig = px.bar(
    df_top_40_growth,
    x=df_top_40_growth["total_commits_last_year"],
    y=df_top_40_growth["project_name"],
    orientation="h",
    color=df_top_40_growth["development_distribution_score"],
    hover_name=df_top_40_growth["git_url"],
    hover_data=["oneliner","topic","git_namespace"],
    color_continuous_scale=color_continuous_scale,
)

fig.update_layout(
    height=1000,  # Added parameter
    xaxis_title="Commit Growth last Year [%]",
    yaxis_title= None,
    title="Projects with the highest Commit Growth",
    coloraxis_colorbar=dict(
    title="DDS",
    ),
    hoverlabel=dict(
    bgcolor="white"
)
)

df_total_score = df_active.nlargest(40, "total_score")

fig = px.bar(
    df_total_score,
    x=df_total_score["total_score"],
    y=df_total_score["project_name"],
    orientation="h",
    range_x=(0.85, 1),
    hover_name=df_total_score["git_url"],
    hover_data=["oneliner","topic","git_namespace"],
    color = df_total_score["development_distribution_score"],
    color_continuous_scale=color_continuous_scale
)

fig.update_layout(
    height=1000,  # Added parameter
    xaxis_title="Total Score",
    yaxis_title=None,
    title="Top Total Score",
    coloraxis_colorbar=dict(
    title="DDS",
    ),   
    hoverlabel=dict(
    bgcolor="white"
)
)
fig.update(layout_showlegend=False)

df_activity_score = df_active.nlargest(40, "activity")

fig = px.bar(
    df_activity_score,
    x=df_activity_score["activity"],
    y=df_activity_score["project_name"],
    orientation="h",
    range_x=(2.9, 3.2),
    hover_name=df_activity_score["git_url"],
    hover_data=["oneliner","topic","git_namespace"],
    color=df_activity_score["development_distribution_score"],
    color_continuous_scale=color_continuous_scale
)

fig.update_layout(
    height=1000,  # Added parameter
    xaxis_title="Activity Score",
    yaxis_title=None,
    title="Projects with the highest Activity Score",
    coloraxis_colorbar=dict(
    title="DDS",
    ),
    hoverlabel=dict(
    bgcolor="white"
)
)

fig.update(layout_showlegend=False)

df_size_score = df_active.nlargest(40, "size")

fig = px.bar(
    df_size_score,
    x=df_size_score["size"],
    y=df_size_score["project_name"],
    orientation="h",
    range_x=(3.75, 4),
    hover_name=df_size_score["git_url"],
    hover_data=["oneliner","topic","git_namespace"],
    color=df_size_score["development_distribution_score"],
    color_continuous_scale=color_continuous_scale
)

fig.update_layout(
    height=1000,  # Added parameter
    xaxis_title="Size Score",
    yaxis_title=None,
    title="Projects with the highest Size Score",
    coloraxis_colorbar=dict(
    title="DDS",
    ),
    hoverlabel=dict(
    bgcolor="white"
)
)

fig.update(layout_showlegend=False)

# KK: I'd suggest selecting a few most interesting examples conveying a message and put plots with raw data in the Appendix

fig = px.scatter(
    df_active.query("project_age_in_years<@max_age_in_years"),
    x="project_age_in_years",
    y="topic",
    size="size",
    color="total_score",
    hover_name="git_url",
    hover_data=["oneliner","topic","git_namespace"],
    size_max=20,
)

fig.update_layout(
    coloraxis_colorbar=dict(title="Total Score"),
    height=1000,  # Added parameter
    xaxis_title="Project Age in Years",
    yaxis_title=None,
    title="Total Score of Projects",
    hoverlabel=dict(
    bgcolor="white"
)
)


fig.show()

# KK: I'd suggest selecting a few most interesting examples conveying a message and put plots with raw data in the Appendix

fig = px.scatter(
    df_organization_projects.query("project_age_in_years<@max_age_in_years"),
    x="project_age_in_years",
    y="topic",
    size="size",
    color="development_distribution_score",
    hover_name="git_url",
    hover_data=["oneliner","topic","git_namespace"],
    size_max=20,
)

fig.update_layout(
    coloraxis_colorbar=dict(
        title="DDS",
    ),
    yaxis_title=None,
    xaxis_title="Project Age in Years",
    height=1000,  # Added parameter
    title="Development Distribution Score",
    hoverlabel=dict(
    bgcolor="white"
)
)
fig.show()

personal_stargazers = df_personal_projects.nlargest(40, "stargazers_count")

fig = px.bar(
    personal_stargazers,
    x=personal_stargazers["stargazers_count"],
    y=personal_stargazers["git_namespace"],
    orientation="h",
    hover_name=personal_stargazers["git_url"],
    hover_data=["oneliner","topic","git_namespace"],
    color=personal_stargazers["development_distribution_score"],
    color_continuous_scale=color_continuous_scale
)

fig.update_layout(
    height=1000,  # Added parameter
    yaxis_title=None,
    xaxis_title="Stars",
    title="Projects with most Stars in User Namespace",
    coloraxis_colorbar=dict(
    title="DDS",
    ),
    hoverlabel=dict(
    bgcolor="white"
)
)


fig.update(layout_showlegend=False)

# KK: can topics be grouped in fewer categories? can DDS be bucketed into categories, e.g. 0.3>=, 0.3<=&<=0.6, 0.6>=? Do we need to show all three variables, projects, DDS and dependents? 

df_active["dependents_count"] = df_active["dependents_repos"].apply(count_strings)

most_dependent_projects = df_active.nlargest(50, "dependents_count")
most_dependent_projects = most_dependent_projects[most_dependent_projects["project_name"] != "Mission Support System"]
print("DDS of most used Python project:",round(most_dependent_projects["development_distribution_score"].median(),3))


fig = px.bar(
    most_dependent_projects,
    x=most_dependent_projects["dependents_count"],
    y=most_dependent_projects["project_name"],
    orientation="h",
    hover_name=most_dependent_projects["git_url"],
    hover_data=["oneliner","topic","git_namespace"],
    color=most_dependent_projects["development_distribution_score"],
    color_continuous_scale=color_continuous_scale
)

fig.update_layout(
    height=1000,  # Added parameter
    yaxis_title=None,
    xaxis_title="Dependents",
    title="Most used Python Projects vs. DDS",
    coloraxis_colorbar=dict(
    title="DDS",
    ),
    hoverlabel=dict(
    bgcolor="white"
)
)

DDS of most used Python project: 0.436

Process the organizations#

df_organizations = pd.read_csv("./csv/github_organizations.csv")
df_organizations["ISO_3"] = df_organizations["location_country"].apply(name_to_iso3)
df_organizations["ISO_3_alpha2"] = df_organizations["ISO_3"].apply(alpha3_to_alpha2)
df_organizations["continent"] = df_organizations["ISO_3_alpha2"].apply(alpha2_to_continent)

continent_his = df_organizations["continent"].value_counts().to_frame().rename_axis("continent_name")
continent_his.rename(index={"EU": "Europe", "NA": "North America", "": "Global", "OC":"Oceania", "AS":"Asia", "SA":"South America", "AF":"Africa"},inplace=True)

print(continent_his)
fig = px.pie(continent_his.reset_index(), values="continent", names="continent_name", color_discrete_sequence=color_discrete_sequence, hole=0.2)

fig.update_layout(title="Distribution of Organizations between Continents", font_size=16, showlegend=False)
fig.update_traces(textposition='outside', textinfo='label+percent', marker=dict(line=dict(color='#000000', width=2)))
fig.show()

                continent
continent_name           
Europe                203
North America         191
Global                176
Oceania                19
Asia                   12
South America           6
Africa                  4

# alternative to plotin cell 52

alt_df_organizations = df_organizations.copy()
vals_to_replace = {"EU": "Europe", "NA": "North America", "": "Global", "OC":"Other", "AS":"Other", "SA":"Other", "AF": "Other"}
alt_df_organizations['continent'] = alt_df_organizations['continent'].map(vals_to_replace)

alt_continent_his = alt_df_organizations["continent"].value_counts().to_frame().rename_axis("continent_name")


print(alt_continent_his)
alt_fig = px.pie(alt_continent_his.reset_index(), values="continent", names="continent_name", color_discrete_sequence=color_discrete_sequence, hole=0.2)

alt_fig.update_layout(title="Distribution of Organizations between Continents", font_size=16, showlegend=False)
alt_fig.update_traces(textposition='outside', textinfo='label+percent', marker=dict(line=dict(color='#000000', width=2)))
alt_fig.show()

                continent
continent_name           
Europe                203
North America         191
Global                176
Other                  41

# similar pooling to the one in cell 53 could be done here for Africa + Oceania

fig = px.pie(df_users_continent_cotoverse, values=0, names="index", color_discrete_sequence=color_discrete_sequence, hole=0.2)

fig.update_layout(title="Distribution of Users between Continents", font_size=16, showlegend=False)
fig.update_traces(textposition='outside', textinfo='label+percent', marker=dict(line=dict(color='#000000', width=2)))
fig.show()

organization_his = (
    df_organizations["form_of_organization"]
    .value_counts()
    .to_frame()
    .rename_axis("organization")
    .reset_index()
)

organization_his["organization"] = organization_his["organization"].apply(upper_string)
fig = px.pie(organization_his, values="form_of_organization", names="organization", color_discrete_sequence=color_discrete_sequence, hole=0.2)

fig.update_layout(title="Distribution of Organizational Forms", font_size=16, showlegend=False)
fig.update_traces(textposition='outside', textinfo='percent+label', marker=dict(line=dict(color='#000000', width=2)))
fig.show()

df_countries = (
    df_organizations["ISO_3"]
    .value_counts()
    .to_frame()
    .rename_axis("country")
    .reset_index()
)
df_countries = df_countries.rename(columns={"ISO_3": "counts"})

fig = px.choropleth(
    df_countries,
    locations="country",
    locationmode="ISO-3",
    color="counts",
    color_continuous_scale=color_continuous_scale
)

fig.update_layout(title="Distribution of Organizational Locations Worldwide",
                    coloraxis_colorbar=dict(
                    title="Organizations",
                    ),)

fig.show()

df_public_repos = df_organizations.nlargest(40, "organization_public_repos")
df_organizations["organizations_age_in_years"] = df_organizations["organization_created"].apply(calc_age)
fig = px.scatter(
    df_organizations.query("organizations_age_in_years<@max_age_in_years"),
    x="organizations_age_in_years",
    y="location_country",
    size="organization_public_repos",
    color="form_of_organization",
    hover_name="organization_website",
    hover_data=["organization_name"],
    size_max=20,
    color_continuous_scale=color_continuous_scale,
).for_each_trace(lambda t: t.update(name=t.name.split("=")[1]))


fig.update_layout(
    coloraxis_colorbar=dict(
        title="DDS",
    ),
    yaxis_title=None,
    xaxis_title="Project Age in Years",
    height=1000,  # Added parameter
    title="Organizations forms within different countries",
    hoverlabel=dict(
    bgcolor="white"
)
)
fig.show()

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
Cell In [43], line 3
df_public_repos = df_organizations.nlargest(40, "organization_public_repos")
df_organizations["organizations_age_in_years"] = df_organizations["organization_created"].apply(calc_age)
----> 3 fig = px.scatter(
   df_organizations.query("organizations_age_in_years<@max_age_in_years"),
   x="organizations_age_in_years",
   y="location_country",
   size="organization_public_repos",
   color="form_of_organization",
   hover_name="organization_website",
   hover_data=["organization_name"],
   size_max=20,
   color_continuous_scale=color_continuous_scale,
).for_each_trace(lambda t: t.update(name=t.name.split("=")[1]))
fig.update_layout(
   coloraxis_colorbar=dict(
       title="DDS",
   (...)
)
)
fig.show()

File /opt/hostedtoolcache/Python/3.8.14/x64/lib/python3.8/site-packages/plotly/graph_objs/_figure.py:810, in Figure.for_each_trace(self, fn, selector, row, col, secondary_y)
def for_each_trace(
   self, fn, selector=None, row=None, col=None, secondary_y=None
) -> "Figure":
   """

   Apply a function to all traces that satisfy the specified selection
   (...)

   """
--> 810     return super(Figure, self).for_each_trace(fn, selector, row, col, secondary_y)

File /opt/hostedtoolcache/Python/3.8.14/x64/lib/python3.8/site-packages/plotly/basedatatypes.py:1306, in BaseFigure.for_each_trace(self, fn, selector, row, col, secondary_y)
"""
Apply a function to all traces that satisfy the specified selection
criteria
   (...)
   Returns the Figure object that the method was called on
"""
for trace in self.select_traces(
   selector=selector, row=row, col=col, secondary_y=secondary_y
):
-> 1306     fn(trace)
return self

Cell In [43], line 13, in <lambda>(t)
df_public_repos = df_organizations.nlargest(40, "organization_public_repos")
df_organizations["organizations_age_in_years"] = df_organizations["organization_created"].apply(calc_age)
fig = px.scatter(
   df_organizations.query("organizations_age_in_years<@max_age_in_years"),
   x="organizations_age_in_years",
   y="location_country",
   size="organization_public_repos",
   color="form_of_organization",
   hover_name="organization_website",
   hover_data=["organization_name"],
   size_max=20,
   color_continuous_scale=color_continuous_scale,
---> 13 ).for_each_trace(lambda t: t.update(name=t.name.split("=")[1]))
fig.update_layout(
   coloraxis_colorbar=dict(
       title="DDS",
   (...)
)
)
fig.show()

IndexError: list index out of range

Open Source in Environmental Sustainability

Open Source in Environmental Sustainability

Contents

Open Source in Environmental Sustainability#

Process the organizations#